AI in the music industry

It remains up for debate whether computers are capable of creativity, but that has not stopped artificial intelligence pioneers from around the world attempting to implement the technology across the creative sphere, from art to music.

Google recently launched its Magenta AI tool to explore “the role of machine learning in the process of creating art and music” and create songs and other works of art itself, but how close are we to seeing original music created by these machines that people actually enjoy?

The vast majority of Western pop music today plays within certain very limited parameters, and the theory goes that if a machine can be taught these parameters and the relationship that exists within them then it should be able to be able to create music that is pleasing to our ears. Music itself is far broader than what makes it into the charts, but to make a simple melody that people enjoy combined with a basic four-to-the-floor rhythm should be a much easier nut to crack. And the technology is coming on very quickly as you can tell from the melody below, which was created by Magenta and already sounds like the opening to an 80s-themed TV show.

Futurologists like Blake Rubin have long understood that as processor power continues to grow, the utility of artificial intelligence will soon outgrow the limited industries within which it was first imagined, like manufacturing, and move into the more “human” realm of creativity. And as companies spend billions of dollars on developing these tools around the world, the day when we will not only mistake bots for people online, but mistake art made by machines for that made by artists and musicians, is soon to be upon us.

Outside of pure creativity, many tech firms are already using machine learning and other forms of artificial intelligence in their products, whether that is to suggest the next film to watch on Netflix, or further articles on a news website, or new people to follow on social media. The computer determines what sort of things you have liked in the past and then extrapolates that out to guess what you might like in the future, using data both from other people like you and the meta data of the object being recommended.

Taking Spotify as an example, whilst millions of users have used the streaming platform to create and share their own playlists, Spotify itself also provides AI-created playlists of music it thinks you might like from its stable of 20+ million tracks. For some, these recommendations are great as they let people move beyond their own, often limited, experience of bands and artists to find somebody new. However, for those of us that surround ourselves with new music every day, these playlists still miss one thing – humanity – that personal recommendation form a friend that will let you jump outside of your comfort zone into something new that you otherwise would never find out about. This pathway jump is something that computers have yet to figure out, and is the reason that those human curated playlists remain significantly more interesting than the computer generated ones. Can computers ever help us jump outside our comfort zone, or are they only ever able to narrow our artistic pleasures – time will tell.